Cell-type-specific 3D reconstruction of five neighboring barrel columns in rat vibrissal cortex (credit: Marcel Oberlaender et al.) It is well established that there is a functional columnar structure repeated throughout the cortex across species (the concept received significant attention after a study by Mountcastle in 1957 [1]; see [2] and… Read More »Mini, macro, micro and hyper columns: confusing terminology
by David Rawlinson and Gideon Kowadlo Why the retina is worth a deeper look Recently, we have been looking at the retina – light sensitive cell layers inside the eyeball, that detect wavelength and intensity, and compute some initial encoding of this data that is transmitted to the brain proper… Read More »Another look at the retina
This might be our new test suite for the algorithms! http://www.arcadelearningenvironment.org/ “The Arcade Learning Environment (ALE) is a simple object-oriented framework that allows researchers and hobbyists to develop AI agents for Atari 2600 games. It is built on top of the Atari 2600 emulator Stella and separates the details of… Read More »The Arcade Learning Environment – a test suite for AGI
Just a few days ago, Jay posted here about the recent public attention to AI and the potential future dangers that it presents. Discussion on the topic continues, but it’s not all talk. There are at least two institutes focussed on practical steps to mitigate the risks. There is an… Read More »Mitigating the perceived future risks of AI
By Punarjay Chakravarty In the past year or so, there has been a spate of high-profile pronouncements by respected scientists and engineers, cautioning the world about the potential ill-effects of AI. Stephen Hawking, one of the foremost experts on theoretical and astro physics told the BBC that “The development of… Read More »The Real Risks of AI
I recently published a post on the question of classifying ‘no input’ or missing data. There was a comprehensive reply by Fergal Byrne in a thread on the Nupic Theory mailing list. I am re-posting it here (below) as it provides a valuable perspective of the discussion in the context of human cortical function. Jan 24th… Read More »A response to “when is missing data a valid state?”
If you’re looking for a formal and precise description of the current HTM/CLA algorithm, then this blog [1, 2] by Fergal Byrne may be of interest. Our experience is that while pseudocode is a good way of describing an algorithm, equations can often be more succinct and specific. The CLA… Read More »Mathematics of HTM by Fergal Byrne
By Gideon Kowadlo, David Rawlinson and Alan Zhang Can you hear silence or see pitch black? Should we classify no input as a valid state or ignore it? To my knowledge, the machine learning and statistics literature mainly regards an absence of input as missing data. There are several ways… Read More »When is missing data a valid state?
DeepMind, based on a deep learning architecture made a big impact in the tech industry. Deep learning continues to gain traction in the machine learning community and in the mainstream, both in terms of public awareness of its capabilities as well as through commercialisation. This article by Simon Winder provides… Read More »Deep learning, a history in context
TL;DR: An SDR is a Sparse Distributed Representation, described below SDRs are biologically plausible data structures SDRs have powerful properties SDRs have received a lot of attention recently There are a few really great new resources on the topic: Presentation by Subutai Ahmad of Numenta Older introductory presentation by Jeff Hawkins Excellent… Read More »Sparse Distributed Representations (SDRs)