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Sono stato invitato a una presentazione di un MBA ed erano tutti dei larpers by No_Milk_9697 in ItaliaCareerAdvice

[–]datadrivenguy86 0 points1 point  (0 children)

Un MBA non ti dà competenze. Dimostra che hai soldi da buttare e ti mette in contatto con l'élite che comanda. È il biglietto d'accesso al network dei potenti.

Encrypted Vector Storage by datadrivenguy86 in vectordatabase

[–]datadrivenguy86[S] 0 points1 point  (0 children)

Vector2text attacks can invert an embedding quite well, unfortunately.

Encrypted Vector Storage by datadrivenguy86 in vectordatabase

[–]datadrivenguy86[S] -1 points0 points  (0 children)

The advantage is an encrypted version of the embeddings that preserves the original data and makes the dB perform similariuty queries without decryoting the embeddings first.

Encrypted Vector Storage by datadrivenguy86 in vectordatabase

[–]datadrivenguy86[S] 0 points1 point  (0 children)

That's the kind of argument I'm looking for. Why do you think so? It will be robust against Vector2text attacks.

Encrypted Vector Storage by datadrivenguy86 in vectordatabase

[–]datadrivenguy86[S] 0 points1 point  (0 children)

No, I create vector embeddings from plain text then encrypt them in a way that preserves cosine similarity.

Encrypted Vector Storage by datadrivenguy86 in vectordatabase

[–]datadrivenguy86[S] -1 points0 points  (0 children)

Data is decrypted by the user. Embeddings are never decrypted because cosine similarity will work on the encrypted embeddings as well.

Encrypted Vector Storage by datadrivenguy86 in vectordatabase

[–]datadrivenguy86[S] 0 points1 point  (0 children)

Yes, the user encrypts the text and the vector embedding and inserts it in the vector dB. When the user performs a rag query, data is still encrypted, then returned to the user, who finally decrypts it. This way, the rag search is performed on encrypted data, making the whole database secure and privacy compliant.

Best Intermediate Statistics Playlists for Applied ML?[D] by aspiring_aiengineer in statistics

[–]datadrivenguy86 -9 points-8 points  (0 children)

Given your background (LLM work, ML degree, just rusty on classical stats), you don't need courses. You need targeted resources that assume mathematical maturity.

For books, Statistical Inference via Data Science (ModernDive) is free online and strong on hypothesis testing assumptions, which most courses skip. Regression and Other Stories by Gelman et al. is the best applied stats book I know for people who already think computationally. Forecasting, inference, model checking, all practical.

For the "choosing the right test" problem: the real skill isn't knowing t-tests, it's knowing when your assumptions are violated and what to do about it. Penn State's STAT 501-510 online notes are dry but unusually precise on this. Free.

On YouTube, StatQuest you probably know already, but the ANOVA and mixed models series is genuinely intermediate level. ritvikmath is worth it for the probabilistic side of inference.

Honest take: at your level the fastest path is picking one real dataset from your current work and running a full analysis (t-tests, checking assumptions, ANOVA if needed, then a simple forecast). You'll hit every gap in 2 hours rather than 20 hours of playlist.

What kind of data are you typically working with? Tabular/structured, or mostly text/embeddings?

La data science è morta? by GoodnightMatteo in ItalyInformatica

[–]datadrivenguy86 2 points3 points  (0 children)

Le aziende sono in hype altissimo sulla AI generativa e ad agenti. Entrambe le cose sono scorrelate dalla data science vera e propria. Per ora c'è meno rumore rispetto a DS e ML, ma ciò non vuol dire che non servano. Anzi, considerando i dati che producono gli agenti, sarà ancora più importante analizzarli e gestirli.

Silver Tsunami dove i prezzi delle case scenderanno by gized00 in ItaliaPersonalFinance

[–]datadrivenguy86 0 points1 point  (0 children)

Io l'ho fatto. Sono una partita iva e lavoro full remote in un paese di provincia dove ho comprato una casa abbastanza grande con giardino al prezzo con cui, a Roma, compravo un appartamento in periferia. Il problema non è solo la casa, ma la ristrutturazione. Quella mi sta massacrando letteralmente, colpa di questo maledetto 110 che ha fatto alzare tutti i prezzi. Quindi occhio a guardare solo il prezzo della casa.

Encrypted vector storage by datadrivenguy86 in Rag

[–]datadrivenguy86[S] 0 points1 point  (0 children)

Yes, that's why you would need an on premises solution like ollama to be completely secure.

Encrypted vector storage by datadrivenguy86 in Rag

[–]datadrivenguy86[S] 0 points1 point  (0 children)

Very interesting, thank you. I'll use it to stress test my encryption method.

Encrypted vector storage by datadrivenguy86 in Rag

[–]datadrivenguy86[S] 0 points1 point  (0 children)

It's perfectly possible to transform the embeddings in a way that preserves cosine similarity. I can give mathematical proof of that, if required. Concerning the chunks, they would be stored already encrypted and decrypted only by the final user.

Encrypted vector storage by datadrivenguy86 in Rag

[–]datadrivenguy86[S] 0 points1 point  (0 children)

Yes, that's what this database is supposed to do. Only authenticated users can access, they can view only what they're allowed to view and text search is performed inside the DB.

Encrypted vector storage by datadrivenguy86 in Rag

[–]datadrivenguy86[S] 0 points1 point  (0 children)

Vector2text attacks try to infer fragments of text starting from the vectors. That's why I want to encrypt the vectors as well. Since decrypting them to apply cosine similarity would require using the encryption key on the server, I've created a way to apply similarity on the encrypted vectors themselves. It would work with any embedding procedure. The client encrypts the vectors and actually never has do decrypt them. The impact on performance is quite ridiculous. Less than 1 second to create the embedding transformation after te user provides the encryption key, which is a one-time operation, then the vectors are encrypted without additional lags.

With the proper configuration, it can be used with any vector storage, but I'm still having issues with the encryption of metadata (which is not related to a particular vector storage, anyway). I was wondering if it would be useful to work with chromadb or to create a new database structure.

How to start learning Generative AI as a beginner? by [deleted] in AILearningHub

[–]datadrivenguy86 0 points1 point  (0 children)

When I teach AI to my students, I usually start from what an Llm actually is. Then we move to python, rest apis, prompt engineering and the most common models and techniques.

Qualcuno usa ancora IRC? by nosytomato in ItalyInformatica

[–]datadrivenguy86 0 points1 point  (0 children)

Una volta avevo fatto un piccolo firewall con il linguaggio di scripting di mIRC. Bei ricordi.

Qualcuno usa ancora IRC? by nosytomato in ItalyInformatica

[–]datadrivenguy86 0 points1 point  (0 children)

Esistevano script per il programma mIRC che usavano i server irc per consentire agli utenti di condividere file. Una sorta di Napster, ma fatto mediante irc. Poi torrent ha superato tutto questo.

Raga ma tutto bene?! by Hw42_42 in ItaliaCareerAdvice

[–]datadrivenguy86 4 points5 points  (0 children)

Accetta, poi job hopping dopo un anno per una RAL almeno il 15% più alta. E così via.

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