🛑 𝗜𝘁’𝘀 𝗻𝗲𝘃𝗲𝗿 𝘁𝗼𝗼 𝗹𝗮𝘁𝗲 𝘁𝗼 𝗴𝗼 𝗽𝗿𝗶𝘃𝗮𝘁𝗲—𝗵𝗲𝗿𝗲’𝘀 𝘄𝗵𝘆 “𝘂𝗻𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴” 𝗳𝗮𝗹𝗹𝘀 𝘀𝗵𝗼𝗿𝘁

📉 Harvard/MIT: 91% of real-world models suffer data drift and need retraining anyway.
⚙️ UNC + DeepMind (2024): exact unlearning still needs several complete fine-tune passes per deletion—massive compute overhead.

Stack those two facts and every delete-request turns into an expensive, repetitive fire drill.

𝘉𝘦𝘵𝘵𝘦𝘳: keep sensitive data out of the weights from the start. 𝗖𝗢𝗡𝗙𝗦𝗘𝗖’s attested enclaves make prompts private on day one—no unlearning treadmill later.

#PrivacyByDesign #DataDrift #MachineUnlearning #ConfidentialComputing

🤖 #MachineUnlearning: un sogno quasi impossibile di cancellarsi dalla memoria dell'IA. Un viaggio tra privacy e tecnologia. #IAPrivacy

🔗 https://www.tomshw.it/business/cancellati-dalla-memoria-dellia-il-sogno-quasi-impossibile-del-machine-unlearning

I don't think this creative writing is common research practice, but I definitely like it...

The paper's called "Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks", and it's from an actual IEEE journal. https://doi.org/10.1109/CVPR42600.2020.00932

#academia #AcademicChatter #computerScience #neuralnetwork #ieee #cvpr #cvpr2020 #machineLearning #machineUnlearning

Machine unlearning poses challenges as #AI advances. Getting neural networks to "forget" is hard since facts aren't neatly stored. Unlearning can degrade performance. But it's necessary for enforcing copyright, bias removal & privacy.

Training on unlicensed data raises legal issues. There's more work needed on responsible unlearning. What role should it play in ethical AI?

https://www.axios.com/2024/01/12/ai-forget-unlearn-data-privacy

#machinelearning #ai #machineunlearning

#AI #ML #MU #MachineUnlearning #TrustworthyAI #RightToBeForgotten: "Machine Unlearning (MU) is often analyzed in terms of how it can facilitate the “right to be forgotten.” However, in this Perspective, we show that MU can support the OECD’s five principles for trustworthy AI, which are influencing AI development and regulation worldwide. We also argue that the implementation of MU is not without ethical risks. To address these concerns and amplify the positive impact of MU, we offer policy recommendations across six categories to encourage the research and uptake of this potentially highly influential new technology."

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4643518

Everyone talks about machine learning. But what about machine unlearning? 🤔

With the rise of copyright lawsuits and ethical questions around the use of ChatGPT & Co, it's crucial that AI tools can learn how to forget any incorrect or unethically sourced data.

How this might work and how much it'll cost is covered in this interesting article. Worth a read!

https://venturebeat.com/ai/machine-unlearning-the-critical-art-of-teaching-ai-to-forget/

#MachineLearning #MachineUnlearning #AITools #ChatGPT #xl8 #ContentWriting