Milan, 11 November 2017
IBM speech recognition is on the verge of super-human accuracy [Business Insider, 2017]
Artificial Intelligence Beats 'Most Complex Game Devised by Humans' [LiveScience, 2016]
I know there's a proverb which that says 'To err is human,' but a human error is nothing to what a computer can do if it tries.
--- Agatha Christie
People worry that computers will get too smart and take over the world, but the real problem is that they’re too stupid and they’ve already taken over the world.
--- Pedro Domingos
DL is not magic - it is an incredibly powerful tool for extracting regularities from data according a given objective.
Corollary #1: A DL program will be just as smart as the data it gets.
Corollary #2: A DL program will be just as smart as the objective it optimizes.
Can convert words to vectors of numbers - at the hearth of most NLP applications with deep learning
Bolukbasi, T., Chang, K.W., Zou, J., Saligrama, V. and Kalai, A., 2016. Quantifying and reducing stereotypes in word embeddings. arXiv preprint arXiv:1606.06121.
Hundreds of papers were published before this was openly discussed!
This is because gender biases probably account for an increase in testing accuracy.
Recent years have brought extraordinary
advances in the technical domains of AI. Alongside such efforts, designers and researchers from a range of disciplines need to conduct what we call social-systems analyses of AI. They need to assess the impact of technologies on their social, cultural and political settings
--- There is a blind spot in AI research, Nature, 2016
The rise of the racist robots [New Statesman, 2016]
[an investigation] found that the proprietary algorithms widely used by judges to help determine the risk of reoffending are almost twice as likely to mistakenly flag black defendants than white defendants [There is a blind spot in AI research]
Attacking discrimination with smarter machine learning [Google Research Blog]
--- Andrej Karpathy blog
Moosavi-Dezfooli, S.M., Fawzi, A., Fawzi, O. and Frossard, P., 2016. Universal adversarial perturbations. arXiv preprint arXiv:1610.08401.
Jia, R. and Liang, P., 2017. Adversarial examples for evaluating reading comprehension systems. arXiv preprint arXiv:1707.07328.
De Montjoye, Y.A., Radaelli, L. and Singh, V.K., 2015. Unique in the shopping mall: On the reidentifiability of credit card metadata. Science, 347(6221), pp.536-539.
Given access to a black-box classifier, can we infer whether a specific example was part of the training dataset?
We can with shadow training:
Shokri, R., Stronati, M., Song, C. and Shmatikov, V., 2017, May. Membership inference attacks against machine learning models. In 2017 IEEE Symposium on Security and Privacy (SP), (pp. 3-18). IEEE.
Hitaj, B., Ateniese, G. and Perez-Cruz, F., 2017. Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning. arXiv preprint arXiv:1702.07464.
Machine learning offers a fantastically powerful toolkit for building useful complex prediction systems quickly. ... it is dangerous to think of these quick wins as coming for free. ... it is common to incur massive ongoing maintenance costs in real-world ML systems. [Risk factors include] boundary erosion, entanglement, hidden feedback loops, undeclared consumers, data dependencies, configuration issues, changes in the external world, and a variety of system-level anti-patterns.
Italian Association for Machine Learning