RESEARCH

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Artificial Narrow Intelligence Versus Artificial General Intelligence Comparison

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@ingliguori

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13 January 2023 14h20

#Infographic: Artificial Narrow Intelligence Vs Artificial General Intelligence by @ingliguori #ArtificialIntelligence #ComputerScience #AI #smartmachines #AGI #NarrowAI #MachineLearning #DeepLearning #DataScience #RPA #NeuralNetworks #Robots #Bot #DigitalTwins #ModelOps #MLOps

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AI Knowledge Hoarding: China and Corporate Secrecy in Science

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@dotcsv

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13 January 2023 14h03

Se refiere a los que se están aprovechando del conocimiento científico que en el mundo de la IA suele ser de libre acceso, pero que luego ni publican sus avances ni tecnologías. A nivel país está claro que se refiere a China. A nivel empresa no sé en quién estarán pensando.

Machine Unlearning Failures: Adaptivity and Request Ordering Limitations

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13 January 2023 12h58

And https://
arxiv.org/abs/2106.04378 by Gupta @crispy_jung @sethvneel @Aaroth Sharifi-Malvajerdi @ChrisWaites which shows that adaptivity and ordering of MU requests can fail to cause a point to be unlearned, even if the requests are fulfilled honestly. 9/n

Camouflage Poisoning Attack on Machine Learning Models

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13 January 2023 12h58

For more details, please check out the paper and the code! And definitely remember the names of the first authors, Waterloo undergrads Jimmy Di (applied to grad schools this year) and Jack Douglas. https://
arxiv.org/abs/2212.10717 https://
github.com/Jimmy-di/camou
flage-poisoning
… 10/10

Defending Against Data Poisoning Through Strategic Point Addition

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13 January 2023 12h58

From a technical perspective, we raise an interesting new question. Usually, one mitigates data poisoning attacks by *removing* training points. But camouflage essentially asks: can you negate data poisoning attacks by *adding* points! Interesting beyond MU. 7/n

Adversarial Attacks on Machine Learning Model Updates Research

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13 January 2023 12h58

We were inspired by previous papers in the "attacking MU" space. See https://
arxiv.org/abs/2109.08266 by Marchant @bipr @ScottAlfeld
, which poisons MU to make it take longer. 8/n

Dataset Poisoning Attack via Malicious Machine Unlearning

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13 January 2023 12h58

Here's how it works. The adversary first poisons the dataset, and the victim trains a model on it. Everything behaves as normal. But the adversary later requests some of their points to be unlearned. Only after the unlearning, then the model behaves in some malicious way. 4/n

Targeted Poisoning Attacks in Machine Learning Models

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13 January 2023 12h58

In this work we focus on targeted poisoning attacks (the goal is to misclassify a specific point in the test set) & used this attack (
https://
arxiv.org/abs/2009.02276) ft @jonasgeiping @wronnyhuang @tomgoldsteincs
. But ours is a proof of concept, neither is intrinsic to the framework. 5/n

Machine Unlearning: Making AI Models Forget Training Data

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13 January 2023 12h58

Machine unlearning (MU): given a trained model, how do you make it "forget" points in its training data? A lot of work has focused on how to do this fast (i.e., without retraining the whole model). See also this nice article by @tsimonite https://
wired.com/story/machines
-can-learn-can-they-unlearn/
… 2/n