My favorite definition of human consciousness is simply our access to a representation of parts of our internal mental states. In this post we’ll elaborate on this definition, from cognitive psychology’s point of view, and discuss a bit about possible applications for machine learning.
In this post I’ll collect some initial thoughts regarding the security of Google’s Federated Learning, which is a method for learning a model on a server where the clients do not send their data, but instead they send an updated model trained on their device with their own data. The main points are:
- Knowing the clients update can give information on his training data.
- Knowing the average of some updates is likely to give information on each user’s update.
- If an attacker can send many updates, he can get information on a specific client.
The first two points are acknowledged briefly in the article.
This paper presents a cunning adversarial example attack on an unknown DNN model, with a small amount of black box calls to the model available (which happen before the input-for-deformation is given). The algorithm is basically to build a different model, an adversarial DNN, with some arbitrary choice of architecture and hyper parameters, and learn the parameters on a data set given by oracle calls to the model. The choice of inputs to the oracle is made iteratively by taking the inputs from the previous iteration and choosing points close by that are the closest to the decision boundary of the last learned adversarial DNN.
I think it may be possible to improve the choice of the new inputs. The best choices for a new input are inputs such that they should have a big impact on the decision boundary, weighted by the probability distribution of possible inputs.
Several thoughts regarding “big impact on the decision boundary”:
- The work is entirely done during preprocess, as the (adversarial) model is known.
- Points near (at) the decision boundary are very good.
- A point on the decision boundary can be approximated in log-time.
- It may be possible to find good measures to the extent that a new input has changed the decision boundary.
- For example, maybe a form of regularization where we motivate changing as many parameters by as much as possible is good enough. (I guess not, but it is very simple to test)
Several thoughts regarding the probability distribution of possible inputs:
- It seems like a very important concept to understand deeply.
- It is probably heavily researched.
- If there is an available training set, it may be possible to approximate the manifold of the probable inputs.
- Maybe GANs can help with this problem.
First, go and read this OpenAI blog post. Read it? good!
In the next 10 minutes, I’ll write as much as I can on my thoughts regarding the claims posed in the above mentioned post.
I have a slight cognitive dissonance.. I got used to thinking that RL is very good, and that the results obtained on the Atari games, for example, are extremely high. However, it seems that Evolution Strategies (ES), as are any type of “local search” methods, are so generic and simple, such that they should be the lowest standard for any machine learning algorithm.
Is it correct to take away from this that overall RL is just not very good, but that it’s success is mostly a story of fast supercomputers?
OpenAI mentions that these kinds of local search methods are not good for supervised learning. This means that we do have some tools which are much better than local search, but that they are not easily transferable.
A different explanation could simply be that the Atari games and OpenAI Gym-type games, are specific examples where RL algorithms are not working well. Maybe due to their small action space?