We’ve uploaded a new paper to arXiv presenting our algorithm for biologically-plausible learning of distant cause & effect using only local and immediate credit assignment. This is a big step for us – it ticks almost all our requirements for a general purpose representation. The training regime is unsupervised &… Read More »Learning distant cause and effect using only local and immediate credit assignment
As AI/ML researchers, we have obviously pondered the risks of AI. We even wrote about it. But what might surprise you is the risks that keep AI/ML folk awake at night aren’t the ones you hear about in the media. We’re not worried about runaway “paperclip maximizers” or “skynet”-style machine… Read More »AI is already harming us – but not the way you think
We recently talked about Capsules networks and equivariances. NB: If you’re not familiar with Capsules networks, read this first. Our primary objective with Capsules networks is to exploit their enhanced generalization abilities. However, what we’ve found instead raises new questions about how generalization can be measured and whether Capsules networks are… Read More »Predictive Capsules Networks – Research update
We are exploring the nature of equivariance, a concept that is now closely associated with the capsules network architecture (see key papers Sabour et al, and Hinton et al). Machine learning representations that capture equivariance must learn the way that patterns in the input vary together, in addition to statistical clusters in… Read More »Understanding Equivariance
This is the second part of our comparison between convolutional competitive learning and convolutional or fully-connected sparse autoencoders. To understand our motivation for this comparison, have a look at the first article. We decided to compare two specific algorithms that tick most of the features we require: K-Sparse autoencoders, and… Read More »Convolutional Competitive Learning vs. Sparse Autoencoders (2/2)
Competitive learning is a branch of unsupervised learning that was popular a long, long time ago in the 1990s. Older readers may remember – the days before widespread use of GSM mobile phones and before Google won the search engine wars! Although competitive learning is now rarely used, it is worth… Read More »Convolutional Competitive Learning vs. Sparse Autoencoders (1/2)
We’ve just uploaded a spin-off research paper to arXiv titled “Sparse Unsupervised Capsules Generalize Better”. So what’s it all about? Capsules Networks You may have heard of Capsules Networks already – if not, have a read of one of these blog articles (here, here, here, or here (EM routing)), watch this video,… Read More »Sparse Unsupervised Capsules Generalize Better
We attended the 2017 10th Conference on Artificial General Intelligence, which was located in our hometown of Melbourne, Australia! Excitingly, the IJCAI 2017 conference is also in Melbourne this week and ICML 2017 was in Sydney this year. In particular, the “Architectures for Generality and Autonomy” workshop may be of interest… Read More »AGI Conference 2017
Today’s post tries to fit the theoretical concept of Predictive Coding with the unusual structure and connectivity of Pyramidal cells in the Neocortex. Pyramidal neurons Pyramidal neurons are interesting because they are one of the most common neuron types in the computational layers of the neocortex. This almost certainly means… Read More »Pyramidal Neurons and Predictive Coding
Typical results from our experiments: Some active cells in layer 3 of a 3 layer network, transformed back into the input pixels they represent. The red pixels are positive weights and the blue pixels are negative weights; absence of colour indicates neutral weighting (ambiguity). The white pixels are the input… Read More »Region-Layer Experiments