Algorithms could increasingly influence human culture, even though we don’t have a good understanding of how they interact with us or each other.
A new study shows that humans can learn new things from artificial intelligence systems and pass them to other humans, in ways that could potentially influence wider human culture.
The study, published on Monday by a group of researchers at the Center for Human and Machines at the Max Planck Institute for Human Development, suggests that while humans can learn from algorithms how to better solve certain problems, human biases prevented performance improvements from lasting as long as expected. Humans tended to prefer solutions from other humans over those proposed by algorithms, because they were more intuitive, or were less costly upfront—even if they paid off more, later. Read More
Daily Archives: May 27, 2022
Manipulating SGC with data ordering attacks
Machine learning is vulnerable to a wide variety of attacks. It is now well understood that by changing the underlying data distribution, an adversary can poison the model trained with it or introduce backdoors. In this paper we present a novel class of training-time attacks that require no changes to the underlying dataset or model architecture, but instead only change the order in which data are supplied to the model. In particular, we find that the attacker can either prevent the model from learning, or poison it to learn behaviours specified by the attacker. Furthermore, we find that even a single adversarially-ordered epoch can be enough to slow down model learning, or even to reset all of the learning progress. Indeed, the attacks presented here are not specific to the model or dataset, but rather target the stochastic nature of modern learning procedures. We extensively evaluate our attacks on computer vision and natural language benchmarks to find that the adversary can disrupt model training and even introduce backdoors. Read More
Rick's Cafe AI 