Artificial neural networks (ANNs) – are conceptually simple; the combination of inputs and weights in a classical ANN can be represented as a single matrix product operation followed by an elementwise nonlinearity. However, as the number of learned parameters increases, it becomes very difficult to train these networks effectively. Most… Read More »Biologically-plausible learning rules for artificial neural networks
One of our key projects is a memory system that can learn to associate distant cause & effect while only using local, immediate & unsupervised credit assignment. Our approach is called RSM – Recurrent Sparse Memory. We recently uploaded a preprint describing RSM. This is the first of several blog… Read More »Learning partially-observable higher-order sequences using local and immediate credit assignment
Originally published in March 2019 in an electronic journal in Japanese Introduction The purpose of this essay is to survey the relationship between decision making and large-scale cerebral networks with regard to conscious access, a purported neural correlate of consciousness, and to provide clues for computational modelling and general understanding… Read More »Cerebral networks for conscious access and decision making
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
Adaptive optimization methods, such as Adam and Adagrad, maintain some statistics over time about the variables and gradients (e.g. moments) which affect the learning rate. These statistics won’t be very accurate when working with sparse tensors, where most of its elements are zero or near zero. We investigated the effects… Read More »Optimization using Adam on Sparse Tensors
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 recently published 2 new ML/neuroscience research projects as part of the Request for Research (RFRs) projects, with WBAI. They’re fascinating topics that have arisen through the relationship with our advisor Elkhonon Goldberg from the Luria Neuroscience Institute.
It’s such a joy to be able to test an idea, go straight to the idea without wrestling with the tools. We recently developed an experimental setup which, so far, looks like it will do just that. I’m excited about it and hope it can help you too, so here it is. We’ll go through the why we created another framework, and how each module in the experiment setup works.
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