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frag

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datascienceathome.com amethix.com

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defragzone.substack.com 10d ago

The Ten Commandments of AI Usage

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www.youtube.com 19d ago

AI is punishing game developers [video]

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www.youtube.com 23d ago

Don't Provoke Russia

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www.youtube.com 1mo ago

This is how agents lie online

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www.youtube.com 1mo ago

Europe has sovereign AI policy. It has no sovereign AI

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defragzone.substack.com 1mo ago

The Propaganda Algorithm

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datascienceathome.com 2mo ago

Attacking LLMs for Fun and Profit

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www.youtube.com 2mo ago

AI Tips and Tricks

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www.youtube.com 2mo ago

AI and Videogames – Conversational NPCs

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datascienceathome.com 3mo ago

Europe, Wake Up

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www.youtube.com 3mo ago

Generative AI in 2016 [video]

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www.youtube.com 3mo ago

AI and Videogames

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www.youtube.com 3mo ago

Energy-Based Models Is All You Need

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www.youtube.com 3mo ago

World Models and JEPA

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www.youtube.com 3mo ago

World Models (Part 1)

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www.youtube.com 3mo ago

LinkedIn Lost in Translation: What CEOs Mean

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www.youtube.com 3mo ago

Open source is broken. AI pulled the trigger

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www.youtube.com 4mo ago

Agentic AI. Demystified

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www.youtube.com 4mo ago

Show HN: Machine learning and AI. Hype not included

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www.youtube.com 4mo ago

You Can't Be a Superpower on Someone Else's Servers

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www.youtube.com 4mo ago

Social media is an ant mill

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www.youtube.com 4mo ago

Apple's Privacy Is a Lie

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www.youtube.com 4mo ago

Zero Ethics AI: My Dystopian Wishlist

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datasciencetalent.co.uk 4mo ago

Tech Is Shooting Itself in the Foot

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www.youtube.com 4mo ago

Productivity Is the New Data Breach

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defragzone.substack.com 4mo ago

The Day an AI Company Told The Pentagon to Go F*** Itself

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www.youtube.com 4mo ago

AI: A Stateless Function on 10k GPUs Pretending to Know You

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www.youtube.com 5mo ago

Programmable Money: The Cage They'll Call Convenience [video]

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www.youtube.com 7mo ago

AGI: The Dream We Should Never Reach [video]

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www.stallman.org 7mo ago

My Small Mouth

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Thank you for noticing! I’ll keep writing my thoughts and express them in clearer English with the tools I (we) currently have. One day, when Italian becomes a global lingua franca, I’ll wow you even more with my fluency :D

Thanks for reading!

You are very wrong. I polish my non-native english with Ollama and deepseek. Content is my own. Like on my podcast (datascienceathome.com) that exists way before GPT was even a name.

Exactly. What I consider a patch and definitely a symptomatic solution is "solved" via agents that search the web (e.g. asking for the weather forecast of this year - in that case the LLM cannot know the year I am referring to, if not via a web search). Generally speaking, LLMs lack direct temporal awareness. Standard models do not model the flow of time unless explicitly. Some models can encode a model of time when trained on sequential video data and rely on external encoders to provide temporal structure. But that is a very narrow application (video in this example). That cannot be considered a generic form of awarenes of time as a concept through which facts can change.

Fair points, and I appreciate the depth of your critique. You’re right—broad appeals to "moral evolution" can feel hollow without actionable solutions or a deeper analysis of the mechanics involved. My intention wasn’t to provide an exhaustive exploration but to spark a discussion, which your comment does beautifully.

Just to clarify: I don’t consider myself a Democrat (in the context of US elections). In fact, if I were American, I definitely wouldn’t have voted for Harris.

I referenced Trump and Meloni as case studies simply because they’re among the most recent examples. That said, I firmly believe two things:

1) The masses have an incredibly short memory. 2) It’s not just the right that engages in this kind of manipulation.

Exactly. The tests are concerned with integrating the ML component into the broader picture of requirements and engineering. Engineers are not really interested in why the statistics of a model is failing. And should never be (that's why testing model internals in TFML is an anti-pattern)

the entire project is on github. In the folder data/ there are some samples to "see" it in action. Otherwise you have to train it on your voice and apply to whatever sound you like

Exactly! With just 5 epochs and some hours on a low budget GPU I got 81% accuracy. Not bad at all, considering that no knowledge of MFC & Co. is required.

That's right. This is not state of the art. LSTM is good for sequences and sequential data (like audio). But with the approach hereby described, prediction can be done in parallel and asynchronously. Which is something ;)