Luis Saiz Gimeno HTTP 301 🦋

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Luis Saiz Gimeno HTTP 301 🦋

Luis Saiz Gimeno HTTP 301 🦋

@lsaiz

Telecomm. Eng. - Cryptography - Sys.Sec - Info.Sec - Tech. Fraud Prevention - Fraud Prevention Tech. - Global Security Center - Innovation in Security @BBVA

Madrid Katılım Temmuz 2010
4.9K Takip Edilen3.1K Takipçiler
Wouldiwas Shookspeared
Wouldiwas Shookspeared@_LTJorge·
@adamo_es es posible que haya una caída en los alrededores de Madrid, o al menos en la zona de la Sagra? Después de comprobar que no tenía ruta he reiniciado la ONT, y ahora el router no adquiere IP por DHCP.
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nature
nature@Nature·
‘A place of joy’: why scientists are joining the rush to Bluesky go.nature.com/4fDjzsC
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News from Science
News from Science@NewsfromScience·
After recent changes to Elon Musk’s X, Bluesky has rapidly emerged as the new online gathering place for researchers. bit.ly/40UT4u3
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Leonid Bezvershenko
Leonid Bezvershenko@bzvr_·
🚨 We discovered two malicious Python packages in #PyPI repository that remained undetected for over a year. These packages mimicked tools for working with popular AI language models (#ChatGPT and #Claude), silently exfiltrating data and compromising developer environments. Full details and IOCs in the thread 👇
Leonid Bezvershenko tweet media
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Alberto H
Alberto H@Alberto_H9·
Can #AI learn and produce its own emotions, like natural ones? 🤖❤️ Meet LOVE (Latest Observed Values Encoding), a generic self-learning emotional framework for machines. Paper in Nature - Scientific Reports (open access): nature.com/articles/s4159… See how it works! 🧵⬇️
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Pau Rodríguez
Pau Rodríguez@prlz77·
I’m thrilled to announce 3 #internship openings @Apple ML Research in beautiful ☀️ #Barcelona ☀️ for 2025! Two internships on Generative Models (GM), Controllability, Interpretability, and Model Editing; and one on GM &🔈Spatial Audio. Apply: jobs.apple.com/en-us/details/… Details 🧵
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Christian S. Perone
Christian S. Perone@tarantulae·
New article: "The geometry of data: the missing metric tensor and the Stein score" (blog.christianperone.com/2024/11/the-ge…). I show how you can derive a (efficient to compute) data manifold metric tensor with the Stein score alone ! Deep connections to diffusion, score-based models and physics.
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Christian S. Perone
Christian S. Perone@tarantulae·
In the data manifold, the shortest path between two points is a geodesic that pass through high density regions of data. Just like mass curves the space geometry, data also curves the space. In the example below we can see a geodesic being optimized between two points. 1/3
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Clive Chan
Clive Chan@itsclivetime·
wtf is in apple silicon? my single threaded code is 3x faster on macbook than on server while burning like half the power. if apple bothered making a 64-core part they'd take over every datacenter
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vittorio
vittorio@IterIntellectus·
how do you even defend from an army of these chasing you
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Yizheng Chen
Yizheng Chen@surrealyz·
I am recruiting PhD students & Postdocs on AI Security, LLM Agents, Code Generation research at UMD Computer Science @umdcs & Maryland Cybersecurity Center @CollegeParkMC2 For PhD program pls mention me in your application cs.umd.edu/grad/apply. For Postdocs please email me.
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Luiza Jarovsky, PhD
Luiza Jarovsky, PhD@LuizaJarovsky·
🚨 "Fair Enough AI," by Tal Zarsky & @JaneYakowitz, discusses the lack of concrete fairness standards and the inevitable tradeoffs in fairness decisions, and it's a MUST-READ for everyone in AI. It's full of 🌶spicy statements🌶: "Given the cross-cutting goals and societal aspirations that affect how decision-making will be perceived, defining and creating a “fair” algorithm is primarily a policy task rather than a matter of technology or pure logic. This fact has been absorbed in the legal scholarship for some time. The trouble is, recent AI regulatory frameworks have demonstrated an unwillingness to state which types of unfairness will be tolerated in order to avoid other forms of unfairness. Implementing one measure to promote fairness might at time generate or exacerbate fairness on another dimension. 🌶 We suspect that vagueness and abdication of decision-making will be a feature of the AI public policy debates for the foreseeable future. 🌶 Setting priorities not only raises disagreements between regulators, it causes a good deal of heartburn for each individual lawmaker, too, who will have to answer to media inquiries, firms, and voters who come armed with examples of bias, opaqueness, inaccuracy, and privacy intrusions which will follow, no matter what option she chooses. 🌶The public is not prepared for a frank admission that it is acceptable for a large AI company to decide, in advance, that it is ok to implement an algorithm that will be wrong more often for one group than another. 🌶 Nor is it prepared to hear that the same company decided in advance to reduce accuracy for everybody in order to relieve some forms of bias (but not all)" - "🌶 Some charges of unfairness are more valid than others. An accusation that an algorithm is inaccurate, biased, overly opaque, or too gamable will be valid if the faults are unnecessary—that is, if they are known or reasonably discoverable and can be corrected without significantly degrading other forms of fairness. Thus, while we have emphasized that ethical tradeoffs must be made during AI design, 🌶 that is only true for applications and designs that have already made every Pareto-efficient improvement. If an AI application needlessly compromises accuracy, bias, or some other aspect of fairness, it deserves criticism. Any time a company can make improvements for minimal costs along the other dimensions of fairness, they should. The criticisms that worry us are those that are made without any attempt to assess whether the perceived problem is easy to fix (without tradeoffs) or is difficult, requiring compromise between values." 👉 Read the full paper below. 👉 To stay informed of the latest AI governance discussions, including 🌶 spicy research papers, join 38,300+ people who subscribe to my AI governance newsletter (below). If you have curious friends in the field, tell them to subscribe!
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Guille Martín
Guille Martín@Farmaenfurecida·
Gente que parece muy bien informada de todo hasta que habla de algo de lo que tú entiendes.
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Jake Williams
Jake Williams@MalwareJake·
This CVSS 9.8 unauthenticated RCE in Kerberos on Windows feels like it's going to get a lot of attention. But honestly, if you don't have "fire drill" patching programs for domain controllers, what are you even doing? msrc.microsoft.com/update-guide/v…
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